Spatiotemporal Articulated Models for Dynamic SLAM
This work addresses the challenge of SLAM in dynamic environments for robotics, representing an incremental improvement by incorporating spatiotemporal models into existing pipelines.
The paper tackles the problem of robot localization and mapping in dynamic environments by proposing an online spatiotemporal articulation model that estimates articulated structure and temporal predictions from passive observations, resulting in a predictive model integrated into a SLAM pipeline to handle previously unexplored dynamic scenes.
We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations. The resulting model can predict future mo- tions of an articulated object with high confidence because of the spatial and temporal structure. We demonstrate the effectiveness of the predictive model by incorporating it within a standard simultaneous localization and mapping (SLAM) pipeline for mapping and robot localization in previously unexplored dynamic environments. Our method is able to localize the robot and map a dynamic scene by explaining the observed motion in the world. We demonstrate the effectiveness of the proposed framework for both simulated and real-world dynamic environments.